Overview

Dataset statistics

Number of variables12
Number of observations1054972
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory96.6 MiB
Average record size in memory96.0 B

Variable types

Numeric12

Alerts

T2M is highly correlated with T2MWETHigh correlation
T2MDEW is highly correlated with T2MWET and 3 other fieldsHigh correlation
T2MWET is highly correlated with T2M and 3 other fieldsHigh correlation
QV2M is highly correlated with T2MDEW and 3 other fieldsHigh correlation
RH2M is highly correlated with T2MDEW and 1 other fieldsHigh correlation
PRECTOTCORR is highly correlated with T2MDEW and 2 other fieldsHigh correlation
WS10M is highly correlated with WS50MHigh correlation
WD10M is highly correlated with WD50MHigh correlation
WS50M is highly correlated with WS10MHigh correlation
WD50M is highly correlated with WD10MHigh correlation
T2M is highly correlated with T2MWETHigh correlation
T2MDEW is highly correlated with T2MWET and 2 other fieldsHigh correlation
T2MWET is highly correlated with T2M and 2 other fieldsHigh correlation
QV2M is highly correlated with T2MDEW and 2 other fieldsHigh correlation
RH2M is highly correlated with T2MDEW and 1 other fieldsHigh correlation
WS10M is highly correlated with WS50MHigh correlation
WD10M is highly correlated with WD50MHigh correlation
WS50M is highly correlated with WS10MHigh correlation
WD50M is highly correlated with WD10MHigh correlation
T2M is highly correlated with T2MWETHigh correlation
T2MDEW is highly correlated with T2MWET and 2 other fieldsHigh correlation
T2MWET is highly correlated with T2M and 2 other fieldsHigh correlation
QV2M is highly correlated with T2MDEW and 3 other fieldsHigh correlation
RH2M is highly correlated with T2MDEW and 1 other fieldsHigh correlation
PRECTOTCORR is highly correlated with QV2MHigh correlation
WS10M is highly correlated with WS50MHigh correlation
WD10M is highly correlated with WD50MHigh correlation
WS50M is highly correlated with WS10MHigh correlation
WD50M is highly correlated with WD10MHigh correlation
T2M is highly correlated with T2MDEW and 3 other fieldsHigh correlation
T2MDEW is highly correlated with T2M and 3 other fieldsHigh correlation
T2MWET is highly correlated with T2M and 3 other fieldsHigh correlation
QV2M is highly correlated with T2M and 3 other fieldsHigh correlation
RH2M is highly correlated with T2M and 2 other fieldsHigh correlation
PS is highly correlated with T2MWET and 1 other fieldsHigh correlation
WS10M is highly correlated with WS50MHigh correlation
WD10M is highly correlated with WD50MHigh correlation
WS50M is highly correlated with WS10MHigh correlation
WD50M is highly correlated with WD10MHigh correlation
Location is highly correlated with PSHigh correlation

Reproduction

Analysis started2022-02-03 15:18:38.650786
Analysis finished2022-02-03 15:20:04.578995
Duration1 minute and 25.93 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

T2M
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4591
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.276111052 × 10-16
Minimum-2.935873262
Maximum2.791726934
Zeros0
Zeros (%)0.0%
Negative493827
Negative (%)46.8%
Memory size8.0 MiB
2022-02-03T20:50:04.823439image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-2.935873262
5-th percentile-1.713046726
Q1-0.6978024505
median0.06213950431
Q30.6443242358
95-th percentile1.722797591
Maximum2.791726934
Range5.727600196
Interquartile range (IQR)1.342126686

Descriptive statistics

Standard deviation1.000000474
Coefficient of variation (CV)1.593344136 × 1015
Kurtosis-0.3977070877
Mean6.276111052 × 10-16
Median Absolute Deviation (MAD)0.6501858169
Skewness-0.06232624826
Sum6.584817136 × 10-10
Variance1.000000948
MonotonicityNot monotonic
2022-02-03T20:50:04.939153image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06452550731061
 
0.1%
0.034700469831042
 
0.1%
0.031121465331042
 
0.1%
-0.020177599121029
 
0.1%
-0.011826588631022
 
0.1%
0.017998448851021
 
0.1%
0.094350544781020
 
0.1%
0.047823486321010
 
0.1%
0.022770454841009
 
0.1%
0.07287651781008
 
0.1%
Other values (4581)1044708
99.0%
ValueCountFrequency (%)
-2.9358732621
< 0.1%
-2.9334872591
< 0.1%
-2.926329251
< 0.1%
-2.9203642431
< 0.1%
-2.9132062341
< 0.1%
-2.9120132321
< 0.1%
-2.9096272291
< 0.1%
-2.9084342282
< 0.1%
-2.9036622221
< 0.1%
-2.9000832171
< 0.1%
ValueCountFrequency (%)
2.7917269342
< 0.1%
2.7833759231
 
< 0.1%
2.7750249131
 
< 0.1%
2.772638911
 
< 0.1%
2.7714459081
 
< 0.1%
2.7607088951
 
< 0.1%
2.7595158933
< 0.1%
2.7583228922
< 0.1%
2.7559368891
 
< 0.1%
2.7547438871
 
< 0.1%

T2MDEW
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4450
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.103492053 × 10-16
Minimum-3.101873774
Maximum1.717897981
Zeros0
Zeros (%)0.0%
Negative549819
Negative (%)52.1%
Memory size8.0 MiB
2022-02-03T20:50:05.065358image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-3.101873774
5-th percentile-1.581186186
Q1-0.7968100063
median-0.0737931103
Q31.044991167
95-th percentile1.374285991
Maximum1.717897981
Range4.819771755
Interquartile range (IQR)1.841801174

Descriptive statistics

Standard deviation1.000000474
Coefficient of variation (CV)9.062144771 × 1015
Kurtosis-1.094488685
Mean1.103492053 × 10-16
Median Absolute Deviation (MAD)0.8805057249
Skewness-0.1260640761
Sum1.07898579 × 10-10
Variance1.000000948
MonotonicityNot monotonic
2022-02-03T20:50:05.191405image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2996321961457
 
0.1%
1.3139493621452
 
0.1%
1.3180399811414
 
0.1%
1.3067907791394
 
0.1%
1.2924736131390
 
0.1%
1.3098587431387
 
0.1%
1.3282665281372
 
0.1%
1.2955415771364
 
0.1%
1.3211079451361
 
0.1%
1.2842923751343
 
0.1%
Other values (4440)1041038
98.7%
ValueCountFrequency (%)
-3.1018737741
< 0.1%
-3.0947151911
< 0.1%
-3.0824433341
< 0.1%
-3.0568769661
< 0.1%
-3.0538090011
< 0.1%
-3.0517636921
< 0.1%
-3.0497183831
< 0.1%
-3.0394918351
< 0.1%
-3.0343785621
< 0.1%
-3.0333559071
< 0.1%
ValueCountFrequency (%)
1.7178979811
 
< 0.1%
1.6831277211
 
< 0.1%
1.6596066621
 
< 0.1%
1.6534707333
< 0.1%
1.6514254241
 
< 0.1%
1.6452894951
 
< 0.1%
1.6422215312
< 0.1%
1.6401762221
 
< 0.1%
1.6371082581
 
< 0.1%
1.6360856032
< 0.1%

T2MWET
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3541
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.489714272 × 10-15
Minimum-3.420179629
Maximum1.816099262
Zeros0
Zeros (%)0.0%
Negative499283
Negative (%)47.3%
Memory size8.0 MiB
2022-02-03T20:50:05.320003image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-3.420179629
5-th percentile-1.725564326
Q1-0.7972337381
median0.1036314473
Q30.9138608061
95-th percentile1.301122974
Maximum1.816099262
Range5.23627889
Interquartile range (IQR)1.711094544

Descriptive statistics

Standard deviation1.000000474
Coefficient of variation (CV)-6.712699831 × 1014
Kurtosis-0.9189655048
Mean-1.489714272 × 10-15
Median Absolute Deviation (MAD)0.840441301
Skewness-0.3961572473
Sum-1.621299539 × 10-9
Variance1.000000948
MonotonicityNot monotonic
2022-02-03T20:50:05.454627image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.97840450081367
 
0.1%
0.90012810511353
 
0.1%
0.96329852971353
 
0.1%
1.0223491441347
 
0.1%
0.95917871941346
 
0.1%
0.98389758121340
 
0.1%
0.98801739151338
 
0.1%
1.0319620351333
 
0.1%
1.0072431731328
 
0.1%
0.9536856391324
 
0.1%
Other values (3531)1041543
98.7%
ValueCountFrequency (%)
-3.4201796291
< 0.1%
-3.3899676861
< 0.1%
-3.3542626641
< 0.1%
-3.3487695831
< 0.1%
-3.3295438021
< 0.1%
-3.3240507221
< 0.1%
-3.2389079751
< 0.1%
-3.2361614351
< 0.1%
-3.2334148951
< 0.1%
-3.2100693031
< 0.1%
ValueCountFrequency (%)
1.8160992621
< 0.1%
1.8119794511
< 0.1%
1.8106061812
< 0.1%
1.8092329111
< 0.1%
1.8078596411
< 0.1%
1.8051131011
< 0.1%
1.8037398312
< 0.1%
1.8023665611
< 0.1%
1.800993291
< 0.1%
1.799620022
< 0.1%

QV2M
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct402
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.551904719 × 10-16
Minimum-1.573043291
Maximum2.375728373
Zeros0
Zeros (%)0.0%
Negative621652
Negative (%)58.9%
Memory size8.0 MiB
2022-02-03T20:50:05.645630image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1.573043291
5-th percentile-1.210512612
Q1-0.8573577263
median-0.341689088
Q31.050616235
95-th percentile1.631915428
Maximum2.375728373
Range3.948771664
Interquartile range (IQR)1.907973962

Descriptive statistics

Standard deviation1.000000474
Coefficient of variation (CV)-2.196883581 × 1015
Kurtosis-1.278053108
Mean-4.551904719 × 10-16
Median Absolute Deviation (MAD)0.678182391
Skewness0.4510459947
Sum-5.217950516 × 10-10
Variance1.000000948
MonotonicityNot monotonic
2022-02-03T20:50:05.767869image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.89486096523
 
0.6%
-0.94330249936486
 
0.6%
-0.92455091256485
 
0.6%
-0.90579932566479
 
0.6%
-0.85735772636477
 
0.6%
-0.84798193286442
 
0.6%
-0.88548510656437
 
0.6%
-0.87610931316407
 
0.6%
-0.83860613946388
 
0.6%
-0.9151751196341
 
0.6%
Other values (392)990507
93.9%
ValueCountFrequency (%)
-1.5730432911
 
< 0.1%
-1.5636674985
 
< 0.1%
-1.55429170422
 
< 0.1%
-1.54335327843
 
< 0.1%
-1.53397748576
 
< 0.1%
-1.524601692113
 
< 0.1%
-1.515225898181
< 0.1%
-1.505850105228
< 0.1%
-1.496474311324
< 0.1%
-1.487098518414
< 0.1%
ValueCountFrequency (%)
2.3757283731
 
< 0.1%
2.28978361
 
< 0.1%
2.2429046331
 
< 0.1%
2.2335288391
 
< 0.1%
2.2225904132
< 0.1%
2.2038388271
 
< 0.1%
2.1944630334
< 0.1%
2.185087243
< 0.1%
2.1757114462
< 0.1%
2.1663356533
< 0.1%

RH2M
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1570
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.758730133 × 10-17
Minimum-1.786739857
Maximum1.810670488
Zeros0
Zeros (%)0.0%
Negative540810
Negative (%)51.3%
Memory size8.0 MiB
2022-02-03T20:50:05.903573image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1.786739857
5-th percentile-1.448259075
Q1-0.9110809255
median-0.04420419805
Q30.8523124681
95-th percentile1.605020543
Maximum1.810670488
Range3.597410345
Interquartile range (IQR)1.763393394

Descriptive statistics

Standard deviation1.000000474
Coefficient of variation (CV)-3.624857909 × 1016
Kurtosis-1.238725376
Mean-2.758730133 × 10-17
Median Absolute Deviation (MAD)0.8782204077
Skewness0.1200188706
Sum-6.677236541 × 10-11
Variance1.000000948
MonotonicityNot monotonic
2022-02-03T20:50:06.030761image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8106704882978
 
0.3%
1.67564411073
 
0.1%
1.6712529981062
 
0.1%
1.6664959711059
 
0.1%
-1.2700535171046
 
0.1%
1.6551522911044
 
0.1%
1.6734485491033
 
0.1%
1.6782055771031
 
0.1%
1.664300421029
 
0.1%
1.6621048691026
 
0.1%
Other values (1560)1042591
98.8%
ValueCountFrequency (%)
-1.7867398571
 
< 0.1%
-1.7845443061
 
< 0.1%
-1.7823487551
 
< 0.1%
-1.7710050751
 
< 0.1%
-1.7684435984
< 0.1%
-1.7662480471
 
< 0.1%
-1.7640524964
< 0.1%
-1.761491022
< 0.1%
-1.7592954693
< 0.1%
-1.7570999183
< 0.1%
ValueCountFrequency (%)
1.8106704882978
0.3%
1.80847493754
 
< 0.1%
1.80627938660
 
< 0.1%
1.8037179159
 
< 0.1%
1.80152235950
 
< 0.1%
1.79932680876
 
< 0.1%
1.79676533255
 
< 0.1%
1.79456978154
 
< 0.1%
1.7923742364
 
< 0.1%
1.79017867965
 
< 0.1%

PRECTOTCORR
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct773
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.517460266 × 10-17
Minimum-0.2605129047
Maximum45.0057265
Zeros0
Zeros (%)0.0%
Negative897729
Negative (%)85.1%
Memory size8.0 MiB
2022-02-03T20:50:06.165892image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-0.2605129047
5-th percentile-0.2605129047
Q1-0.2605129047
median-0.2605129047
Q3-0.2327932235
95-th percentile1.208630199
Maximum45.0057265
Range45.2662394
Interquartile range (IQR)0.0277196812

Descriptive statistics

Standard deviation1.000000474
Coefficient of variation (CV)-1.812428954 × 1016
Kurtosis115.4712268
Mean-5.517460266 × 10-17
Median Absolute Deviation (MAD)0
Skewness8.483594759
Sum-1.339230948 × 10-10
Variance1.000000948
MonotonicityNot monotonic
2022-02-03T20:50:06.317004image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2605129047764664
72.5%
-0.232793223540208
 
3.8%
-0.205073542321928
 
2.1%
-0.177353861115962
 
1.5%
-0.149634179912832
 
1.2%
-0.121914498710878
 
1.0%
-0.094194817479158
 
0.9%
-0.066475136278019
 
0.8%
-0.038755455077363
 
0.7%
-0.011035773866717
 
0.6%
Other values (763)157243
 
14.9%
ValueCountFrequency (%)
-0.2605129047764664
72.5%
-0.232793223540208
 
3.8%
-0.205073542321928
 
2.1%
-0.177353861115962
 
1.5%
-0.149634179912832
 
1.2%
-0.121914498710878
 
1.0%
-0.094194817479158
 
0.9%
-0.066475136278019
 
0.8%
-0.038755455077363
 
0.7%
-0.011035773866717
 
0.6%
ValueCountFrequency (%)
45.00572651
< 0.1%
35.359277441
< 0.1%
34.5276872
< 0.1%
33.003104541
< 0.1%
32.836786452
< 0.1%
31.922036971
< 0.1%
31.506241751
< 0.1%
31.201325261
< 0.1%
31.173605582
< 0.1%
31.14588591
< 0.1%

PS
Real number (ℝ)

HIGH CORRELATION

Distinct719
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.606999976 × 10-14
Minimum-3.51058936
Maximum2.151726279
Zeros0
Zeros (%)0.0%
Negative480645
Negative (%)45.6%
Memory size8.0 MiB
2022-02-03T20:50:06.459931image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-3.51058936
5-th percentile-1.889460636
Q1-0.5398127985
median0.09622813632
Q30.7632954582
95-th percentile1.391579796
Maximum2.151726279
Range5.662315639
Interquartile range (IQR)1.303108257

Descriptive statistics

Standard deviation1.000000474
Coefficient of variation (CV)3.835828475 × 1013
Kurtosis-0.0004442597721
Mean2.606999976 × 10-14
Median Absolute Deviation (MAD)0.6593107251
Skewness-0.6372924143
Sum2.748329786 × 10-8
Variance1.000000948
MonotonicityNot monotonic
2022-02-03T20:50:06.585330image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.08217358933747
 
0.4%
0.072958346023729
 
0.4%
0.057445152493722
 
0.4%
-0.020120815173717
 
0.4%
-0.089930186073700
 
0.4%
-0.0046076216413700
 
0.4%
0.026418765423691
 
0.3%
0.041931958963688
 
0.3%
0.018662168663681
 
0.3%
0.11949792663663
 
0.3%
Other values (709)1017934
96.5%
ValueCountFrequency (%)
-3.510589361
 
< 0.1%
-3.4950761661
 
< 0.1%
-3.487319571
 
< 0.1%
-3.4562931831
 
< 0.1%
-3.4485365861
 
< 0.1%
-3.4330233923
< 0.1%
-3.4175101991
 
< 0.1%
-3.4097536021
 
< 0.1%
-3.4019970052
< 0.1%
-3.3942404081
 
< 0.1%
ValueCountFrequency (%)
2.1517262791
 
< 0.1%
2.1284564895
< 0.1%
2.1129432954
 
< 0.1%
2.0974301022
 
< 0.1%
2.0896735052
 
< 0.1%
2.0819169088
< 0.1%
2.0741603125
< 0.1%
2.0664037155
< 0.1%
2.05864711811
< 0.1%
2.0508905219
< 0.1%

WS10M
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1371
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.586349173 × 10-16
Minimum-1.871661021
Maximum8.377239232
Zeros0
Zeros (%)0.0%
Negative622194
Negative (%)59.0%
Memory size8.0 MiB
2022-02-03T20:50:06.712864image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1.871661021
5-th percentile-1.292239912
Q1-0.6629761275
median-0.170779702
Q30.414871741
95-th percentile1.966225032
Maximum8.377239232
Range10.24890025
Interquartile range (IQR)1.077847869

Descriptive statistics

Standard deviation1.000000474
Coefficient of variation (CV)2.788352237 × 1015
Kurtosis2.695328907
Mean3.586349173 × 10-16
Median Absolute Deviation (MAD)0.5295784325
Skewness1.297584344
Sum3.636992929 × 10-10
Variance1.000000948
MonotonicityNot monotonic
2022-02-03T20:50:06.841283image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.195701043738
 
0.4%
-0.17701003653715
 
0.4%
-0.2580043853654
 
0.3%
-0.16454936753649
 
0.3%
-0.2829257233635
 
0.3%
-0.15208869853607
 
0.3%
-0.2953863923591
 
0.3%
-0.35145940253589
 
0.3%
-0.1333976953587
 
0.3%
-0.2206223783586
 
0.3%
Other values (1361)1018621
96.6%
ValueCountFrequency (%)
-1.8716610218
 
< 0.1%
-1.86543068633
 
< 0.1%
-1.85920035227
 
< 0.1%
-1.85297001734
 
< 0.1%
-1.84673968342
< 0.1%
-1.84050934870
< 0.1%
-1.83427901471
< 0.1%
-1.82804867985
< 0.1%
-1.82181834597
< 0.1%
-1.8155880192
< 0.1%
ValueCountFrequency (%)
8.3772392321
< 0.1%
8.2401718731
< 0.1%
8.0345708351
< 0.1%
8.0158798312
< 0.1%
7.9348854831
< 0.1%
7.9099641451
< 0.1%
7.7728967862
< 0.1%
7.5922170852
< 0.1%
7.4925317331
< 0.1%
7.4863013991
< 0.1%

WD10M
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct35807
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.258789643 × 10-16
Minimum-1.903159126
Maximum1.444977095
Zeros0
Zeros (%)0.0%
Negative450796
Negative (%)42.7%
Memory size8.0 MiB
2022-02-03T20:50:06.968954image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1.903159126
5-th percentile-1.68194697
Q1-0.9884496503
median0.3447768534
Q30.8644114647
95-th percentile1.264137552
Maximum1.444977095
Range3.348136221
Interquartile range (IQR)1.852861115

Descriptive statistics

Standard deviation1.000000474
Coefficient of variation (CV)2.348086095 × 1015
Kurtosis-1.307500071
Mean4.258789643 × 10-16
Median Absolute Deviation (MAD)0.7264279771
Skewness-0.3826601598
Sum4.947651178 × 10-10
Variance1.000000948
MonotonicityNot monotonic
2022-02-03T20:50:07.153647image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6085011862786
 
0.1%
-1.065939022669
 
0.1%
1.027111238669
 
0.1%
-1.903159126598
 
0.1%
0.8556671658529
 
0.1%
-1.484549074410
 
< 0.1%
-0.228718918373
 
< 0.1%
0.7799452586341
 
< 0.1%
0.1898911341322
 
< 0.1%
0.9219005785318
 
< 0.1%
Other values (35797)1049957
99.5%
ValueCountFrequency (%)
-1.903159126598
0.1%
-1.9027870292
 
< 0.1%
-1.9024149315
 
< 0.1%
-1.9023219064
 
< 0.1%
-1.9022288824
 
< 0.1%
-1.9021358576
 
< 0.1%
-1.9020428337
 
< 0.1%
-1.90194980816
 
< 0.1%
-1.90185678419
 
< 0.1%
-1.9017637620
 
< 0.1%
ValueCountFrequency (%)
1.4449770952
 
< 0.1%
1.444884073
 
< 0.1%
1.4447910463
 
< 0.1%
1.4446980214
 
< 0.1%
1.4446049973
 
< 0.1%
1.44451197215
 
< 0.1%
1.44441894818
< 0.1%
1.44432592425
< 0.1%
1.44423289931
< 0.1%
1.44413987538
< 0.1%

WS50M
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1712
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.448412666 × 10-16
Minimum-1.991354344
Maximum7.560989883
Zeros0
Zeros (%)0.0%
Negative561252
Negative (%)53.2%
Memory size8.0 MiB
2022-02-03T20:50:07.284789image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1.991354344
5-th percentile-1.46191488
Q1-0.7530044114
median-0.08896169387
Q30.6468694256
95-th percentile1.74612933
Maximum7.560989883
Range9.552344228
Interquartile range (IQR)1.399873837

Descriptive statistics

Standard deviation1.000000474
Coefficient of variation (CV)-2.899886327 × 1015
Kurtosis0.4837480412
Mean-3.448412666 × 10-16
Median Absolute Deviation (MAD)0.6999369185
Skewness0.5907099696
Sum-4.047251423 × 10-10
Variance1.000000948
MonotonicityNot monotonic
2022-02-03T20:50:07.418046image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.39854917711908
 
0.2%
-0.11139556951903
 
0.2%
-0.33573432541898
 
0.2%
-0.2056178471893
 
0.2%
-0.44790370341876
 
0.2%
-0.14280299531872
 
0.2%
-0.4344433781868
 
0.2%
-0.30432689961867
 
0.2%
-0.57353340671867
 
0.2%
-0.24151204791860
 
0.2%
Other values (1702)1036160
98.2%
ValueCountFrequency (%)
-1.9913543443
 
< 0.1%
-1.98686756918
 
< 0.1%
-1.98238079430
 
< 0.1%
-1.97789401926
 
< 0.1%
-1.97340724428
 
< 0.1%
-1.96892046942
< 0.1%
-1.96443369344
< 0.1%
-1.95994691857
< 0.1%
-1.95546014357
< 0.1%
-1.95097336885
< 0.1%
ValueCountFrequency (%)
7.5609898831
< 0.1%
7.3052437021
< 0.1%
7.0719313951
< 0.1%
6.865539741
< 0.1%
6.7847777881
< 0.1%
6.7623439121
< 0.1%
6.7040158361
< 0.1%
6.6053067831
< 0.1%
6.5559522571
< 0.1%
6.4931374052
< 0.1%

WD50M
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct35916
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.379563432 × 10-17
Minimum-1.914769112
Maximum1.443621346
Zeros0
Zeros (%)0.0%
Negative449390
Negative (%)42.6%
Memory size8.0 MiB
2022-02-03T20:50:07.547454image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1.914769112
5-th percentile-1.688046266
Q1-0.9881910969
median0.3505933087
Q30.8609529644
95-th percentile1.260190435
Maximum1.443621346
Range3.358390458
Interquartile range (IQR)1.849144061

Descriptive statistics

Standard deviation1.000000474
Coefficient of variation (CV)-1.567506123 × 1016
Kurtosis-1.298763647
Mean-6.379563432 × 10-17
Median Absolute Deviation (MAD)0.7173025654
Skewness-0.3923346709
Sum-3.637978807 × 10-12
Variance1.000000948
MonotonicityNot monotonic
2022-02-03T20:50:07.677094image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6043736128600
 
0.1%
1.024230734507
 
< 0.1%
-1.07505487466
 
< 0.1%
-1.914769112434
 
< 0.1%
0.8522759172385
 
< 0.1%
-1.494911991282
 
< 0.1%
0.7763284292265
 
< 0.1%
0.1845164921254
 
< 0.1%
-0.2353406286248
 
< 0.1%
1.19618555240
 
< 0.1%
Other values (35906)1051291
99.7%
ValueCountFrequency (%)
-1.914769112434
< 0.1%
-1.9143026044
 
< 0.1%
-1.9142093024
 
< 0.1%
-1.9141169
 
< 0.1%
-1.91402269911
 
< 0.1%
-1.91392939717
 
< 0.1%
-1.91383609610
 
< 0.1%
-1.91374279423
 
< 0.1%
-1.91364949327
 
< 0.1%
-1.91355619115
 
< 0.1%
ValueCountFrequency (%)
1.4436213463
 
< 0.1%
1.4435280459
 
< 0.1%
1.4434347436
 
< 0.1%
1.44334144223
< 0.1%
1.4432481415
< 0.1%
1.44315483818
< 0.1%
1.44306153714
< 0.1%
1.44296823515
< 0.1%
1.44287493417
< 0.1%
1.44278163222
< 0.1%

Location
Real number (ℝ)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.0345238 × 10-16
Minimum-1.59177408
Maximum1.593860375
Zeros0
Zeros (%)0.0%
Negative527950
Negative (%)50.0%
Memory size8.0 MiB
2022-02-03T20:50:07.785853image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1.59177408
5-th percentile-1.59177408
Q1-1.012567815
median-0.1437584182
Q30.7250509786
95-th percentile1.593860375
Maximum1.593860375
Range3.185634455
Interquartile range (IQR)1.737618794

Descriptive statistics

Standard deviation1.000000474
Coefficient of variation (CV)-9.666287756 × 1015
Kurtosis-1.217596611
Mean-1.0345238 × 10-16
Median Absolute Deviation (MAD)0.8688093968
Skewness0.0009981734138
Sum-1.178538378 × 10-10
Variance1.000000948
MonotonicityNot monotonic
2022-02-03T20:50:07.867978image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-1.5917740888176
8.4%
-1.01256781588105
8.4%
0.725050978688060
8.3%
-1.30217094788058
8.3%
-0.433361550487973
8.3%
1.01465411187889
8.3%
-0.143758418287824
8.3%
-0.722964682787814
8.3%
1.59386037587803
8.3%
1.30425724387792
8.3%
Other values (2)175478
16.6%
ValueCountFrequency (%)
-1.5917740888176
8.4%
-1.30217094788058
8.3%
-1.01256781588105
8.4%
-0.722964682787814
8.3%
-0.433361550487973
8.3%
-0.143758418287824
8.3%
0.145844714187756
8.3%
0.435447846487722
8.3%
0.725050978688060
8.3%
1.01465411187889
8.3%
ValueCountFrequency (%)
1.59386037587803
8.3%
1.30425724387792
8.3%
1.01465411187889
8.3%
0.725050978688060
8.3%
0.435447846487722
8.3%
0.145844714187756
8.3%
-0.143758418287824
8.3%
-0.433361550487973
8.3%
-0.722964682787814
8.3%
-1.01256781588105
8.4%

Interactions

2022-02-03T20:49:58.113663image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:20.867404image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:24.318426image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:27.637793image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:31.007283image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:34.280411image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:37.672304image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:40.938387image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:44.365729image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:47.751468image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:51.212288image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:54.635068image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:58.422729image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:21.281504image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:24.588904image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:27.915612image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:31.283144image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:34.535381image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:37.938153image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:41.217511image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:44.645878image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:48.035883image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:51.495623image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:54.921674image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:58.716568image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:21.553536image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:24.858690image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:28.188087image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:31.560679image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:34.800656image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:38.197098image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:41.502863image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:44.923167image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:48.312114image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:51.774193image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:55.192359image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:59.005056image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:21.836803image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:25.131832image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:28.478671image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:31.832238image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:35.073560image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:38.458632image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:41.787519image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:45.195766image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:48.588074image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:52.057147image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:55.465355image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:59.289067image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:22.123324image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:25.412663image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:28.773373image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:32.117883image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:35.352537image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:38.733265image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:42.091013image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:45.481270image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:48.885809image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:52.342858image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:55.809744image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:59.572489image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:22.423525image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:25.683925image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:29.052456image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:32.389801image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:35.692514image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:39.001302image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:42.384578image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:45.814284image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:49.162465image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:52.646136image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:56.090562image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:59.861778image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:22.699576image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:26.013724image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:29.325523image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:32.662008image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:35.972853image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:39.255014image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:42.669924image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:46.085777image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:49.443388image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:52.935393image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:56.367736image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:50:00.141720image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:22.965010image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:26.285368image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:29.594734image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:32.922154image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:36.253873image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:39.515346image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:42.958068image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:46.351409image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:49.727784image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:53.227198image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:56.656070image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:50:00.413794image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:23.228325image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:26.541405image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:29.857536image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:33.179756image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:36.533954image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:39.773419image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:43.228206image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:46.631630image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:50.011997image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:53.497680image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:56.954411image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:50:00.749419image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:23.496395image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:26.804254image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:30.132188image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:33.451006image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:36.827034image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:40.068030image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:43.501989image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:46.914345image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:50.298569image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:53.780931image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:57.235087image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:50:01.046283image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:23.759352image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:27.082603image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:30.403510image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:33.720876image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:37.115084image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:40.330670image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:43.768060image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:47.190973image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:50.634957image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:54.055343image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:57.515139image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:50:01.328949image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:24.033601image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:27.360959image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:30.676693image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:34.015497image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:37.400853image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:40.665501image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:44.043027image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:47.474367image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:50.924568image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:54.348284image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-03T20:49:57.813454image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-02-03T20:50:07.960731image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-03T20:50:08.107294image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-03T20:50:08.251056image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-03T20:50:08.395114image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-03T20:50:01.514428image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-03T20:50:02.179984image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

T2MT2MDEWT2MWETQV2MRH2MPRECTOTCORRPSWS10MWD10MWS50MWD50MLocation
0-0.2587780.109262-0.074894-0.1807380.031542-0.2605131.267474-1.105330-1.689110-1.201682-1.6995220.725051
10.533375-0.3877480.045954-0.599523-0.929377-0.2605130.631433-0.152089-1.856461-0.582507-1.8803410.145845
20.4820761.2372501.1088651.4787770.7381442.483736-2.8978180.1656580.597059-0.2370250.604374-0.143758
30.6657981.3006551.2571781.5178430.6122660.820555-0.950912-1.167633-0.794215-1.349746-0.8259400.435448
40.5464981.2904281.1802751.4694020.7425351.735304-0.229549-0.6878970.283009-1.0581050.2911600.145845
5-1.512622-0.072770-0.919455-0.3229381.362412-0.260513-0.539813-0.1271670.0369590.4045840.031689-1.012568
6-0.656047-0.821354-0.929068-0.847982-0.611388-0.260513-1.4085520.5083271.201067-0.0665281.182377-0.143758
72.190454-1.3193870.375539-1.133944-1.631222-0.2605130.088472-1.3171611.169625-1.5695971.017980-1.591774
82.007925-1.6118660.073420-1.248016-1.661228-0.2605130.5073281.7419330.7693401.0372190.7642931.304257
9-1.971928-0.996228-1.803841-0.9714300.383562-0.2605131.445876-0.2829260.181798-0.0755010.1755601.304257

Last rows

T2MT2MDEWT2MWETQV2MRH2MPRECTOTCORRPSWS10MWD10MWS50MWD50MLocation
1054962-1.349181-0.426609-1.062275-0.6088990.481996-0.260513-0.105443-0.2081621.1909270.6423831.187042-1.012568
1054963-0.497378-0.377522-0.540432-0.590148-0.311695-0.2605130.4763010.3961810.7922241.4769230.788831-0.722965
1054964-0.676328-1.403244-1.331436-1.152695-1.018297-0.2605131.2209350.5083270.973250-0.0216600.968436-1.591774
1054965-1.748837-0.589211-1.401473-0.7135960.747292-0.177354-0.8578330.477175-0.0087161.607039-0.0137491.593860
10549660.773168-1.103606-0.295990-1.001120-1.347630-0.260513-1.346499-0.2829261.152694-0.1652371.154480-1.012568
1054967-0.228953-0.477742-0.453916-0.666717-0.593092-0.2605131.3295270.4273320.824876-0.1203690.824099-1.302171
10549680.8101511.2423641.3011231.3834570.3722192.372857-0.268332-1.099100-0.654399-1.295904-0.6533320.435448
10549691.8719230.6359291.5043670.449003-0.8949800.238441-0.539813-0.1583190.617525-0.5914810.621728-1.591774
1054970-0.6130990.7504670.1516960.5536991.488290-0.2605131.019263-0.0710940.2795670.8577480.2739931.014654
1054971-0.508115-0.034932-0.316589-0.2948100.088627-0.260513-0.454490-0.0274821.3253480.7096841.323169-1.012568